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Evolving Solutions for Multiobjective Problems and Hierarchical AI
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Author(s): Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK)and Stephen McGlinchey (University of Paisley, UK)
Copyright: 2008
Pages: 11
Source title:
Biologically Inspired Artificial Intelligence for Computer Games
Source Author(s)/Editor(s): Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK)and Stephen McGlinchey (University of Paisley, UK)
DOI: 10.4018/978-1-59140-646-4.ch009
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Abstract
Multi-Objective Problems, MOP, are a class of problems for which different, competing, objectives are to be satisfied and for which there is generally no single best solution – but rather for which a set of solutions may exist which are all equally as good. In commercial real-time strategy, RTS, games, designers put a lot of effort into trying to create games where a variety of strategies and tactics can be employed and where (ideally) no single simple optimal strategy exists. Indeed, a great deal of effort may be spent in ‘balancing’ the game to ensure that the main strategies and units all have effective counters (Rollings and Morris, 1999). It may be the case, then, that RTS games may be considered as MOP. If not in terms of the overall goal of winning the game, which is clearly a single overriding objective, then in terms of the many different objectives that must be met in order to achieve victory. There may be a number of strong, potentially winning strategies, each of which is formed from the combination of a large number of tactical and strategic decisions – and where improvement in one area will lead to increasing a weakness elsewhere.
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